People Localization in a Camera Network Combining Background Subtraction and Scene-Aware Human Detection

  • Tung-Ying Lee
  • Tsung-Yu Lin
  • Szu-Hao Huang
  • Shang-Hong Lai
  • Shang-Chih Hung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6523)


In a network of cameras, people localization is an important issue. Traditional methods utilize camera calibration and combine results of background subtraction in different views to locate people in the three dimensional space. Previous methods usually solve the localization problem iteratively based on background subtraction results, and high-level image information is neglected. In order to fully exploit the image information, we suggest incorporating human detection into multi-camera video surveillance. We develop a novel method combining human detection and background subtraction for multi-camera human localization by using convex optimization. This convex optimization problem is independent of the image size. In fact, the problem size only depends on the number of interested locations in ground plane. Experimental results show this combination performs better than background subtraction-based methods and demonstrate the advantage of combining these two types of complementary information.


Probabilistic occupancy map video surveillance human localization multi-camera surveillance 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. Pattern Analysis and Machine Intelligence 30, 267–282 (2008)CrossRefGoogle Scholar
  2. 2.
    Delannay, D., Danhier, N., De Vleeschouwer, C.: Detection and recognition of sports(wo)men from multiple views. In: Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), pp. 1–7 (2009)Google Scholar
  3. 3.
    Berclaz, J., Fleuret, F., Fua, P.: Multi-camera tracking and atypical motion detection with behavioral maps. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 112–125. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Levi, K., Weiss, Y.: Learning object detection from a small number of examples: The importance of good features. In: CVPR, vol. II, pp. 53–60 (2004)Google Scholar
  5. 5.
    Paisitkriangkrai, S., Shen, C., Zhang, J.: Fast pedestrian detection using a cascade of boosted covariance features. IEEE Trans. Circuits and Systems for Video Technolog 18, 1140–1151 (2008)CrossRefGoogle Scholar
  6. 6.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. I, pp. 886–893 (2005)Google Scholar
  7. 7.
    Chen, Y., Chen, C.: Fast human detection using a novel boosted cascading structure with meta stages. IEEE Trans. Image Processing 17, 1452–1464 (2008)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Tuzel, O., Porikli, F., Meer, P.: Pedestrian detection via classification on riemannian manifolds. IEEE Trans. Pattern Analysis and Machine Intelligence 30, 1713–1727 (2008)CrossRefGoogle Scholar
  9. 9.
    Alahi, A., Boursier, Y., Jacques, L., Vandergheynst, P.: A sparsity constrained inverse problem to locate people in a network of cameras. In: 16th International Conference on Digital Signal Processing, pp. 1–7 (2009)Google Scholar
  10. 10.
    Alahi, A., Boursier, Y., Jacques, L., Vandergheynst, P.: Sport players detection and tracking with a mixed network of planar and omnidirectional cameras. In: Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), pp. 1–8 (2009)Google Scholar
  11. 11.
    Kaewtrakulpong, P., Bowden, R.: An improved adaptive background mixture model for realtime tracking with shadow detection. In: Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems (2001)Google Scholar
  12. 12.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: CVPR, pp. II: 246–252 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tung-Ying Lee
    • 1
  • Tsung-Yu Lin
    • 1
  • Szu-Hao Huang
    • 1
  • Shang-Hong Lai
    • 1
  • Shang-Chih Hung
    • 2
  1. 1.Department of Computer ScienceNational Tsing Hua UniversityHsinChuTaiwan
  2. 2.Industrial Technology Research InstituteIdentification and Security Technology CenterTaiwan

Personalised recommendations